{"id":"https://openalex.org/W4417438721","doi":"https://doi.org/10.1109/access.2025.3645691","title":"Evolving Interpretable Models for Robust Camera Calibration: A Genetic Programming and Symbolic Regression Approach","display_name":"Evolving Interpretable Models for Robust Camera Calibration: A Genetic Programming and Symbolic Regression Approach","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4417438721","doi":"https://doi.org/10.1109/access.2025.3645691"},"language":null,"primary_location":{"id":"doi:10.1109/access.2025.3645691","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3645691","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3645691","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035906595","display_name":"Ricardo S. Casado","orcid":"https://orcid.org/0000-0002-1575-4687"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Ricardo S. Casado","raw_affiliation_strings":["Federal University of São Carlos, São Carlos, São Paulo, Brazil"],"raw_orcid":"https://orcid.org/0000-0002-1575-4687","affiliations":[{"raw_affiliation_string":"Federal University of São Carlos, São Carlos, São Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018241734","display_name":"Linneu Zanco","orcid":null},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Linneu Zanco","raw_affiliation_strings":["Federal University of São Carlos, São Carlos, São Paulo, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of São Carlos, São Carlos, São Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031378142","display_name":"Cleber Moretti","orcid":null},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Cleber Moretti","raw_affiliation_strings":["Federal University of São Carlos, São Carlos, São Paulo, Brazil"],"raw_orcid":"https://orcid.org/0009-0009-8658-4791","affiliations":[{"raw_affiliation_string":"Federal University of São Carlos, São Carlos, São Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057336762","display_name":"Emerson Carlos Pedrino","orcid":"https://orcid.org/0000-0003-3734-3202"},"institutions":[{"id":"https://openalex.org/I177909021","display_name":"Universidade Federal de S\u00e3o Carlos","ror":"https://ror.org/00qdc6m37","country_code":"BR","type":"education","lineage":["https://openalex.org/I177909021"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Emerson C. Pedrino","raw_affiliation_strings":["Federal University of São Carlos, São Carlos, São Paulo, Brazil"],"raw_orcid":"https://orcid.org/0000-0003-3734-3202","affiliations":[{"raw_affiliation_string":"Federal University of São Carlos, São Carlos, São Paulo, Brazil","institution_ids":["https://openalex.org/I177909021"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5035906595"],"corresponding_institution_ids":["https://openalex.org/I177909021"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.39938306,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"213934","last_page":"213961"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10638","display_name":"Optical measurement and interference techniques","score":0.7595000267028809,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10638","display_name":"Optical measurement and interference techniques","score":0.7595000267028809,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.12370000034570694,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.06530000269412994,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/symbolic-regression","display_name":"Symbolic regression","score":0.9350000023841858},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.8539000153541565},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5200999975204468},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.46000000834465027},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.43950000405311584},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.3785000145435333},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.37770000100135803},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.367000013589859}],"concepts":[{"id":"https://openalex.org/C2776400721","wikidata":"https://www.wikidata.org/wiki/Q18171762","display_name":"Symbolic regression","level":3,"score":0.9350000023841858},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.8539000153541565},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7060999870300293},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6295999884605408},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5001999735832214},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.43950000405311584},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3785000145435333},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.37770000100135803},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.31520000100135803},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3093999922275543},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.28209999203681946},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C2776095079","wikidata":"https://www.wikidata.org/wiki/Q489538","display_name":"The Symbolic","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2025.3645691","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3645691","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3645691","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3645691","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1190203938","https://openalex.org/W1542000206","https://openalex.org/W1591435035","https://openalex.org/W1599444632","https://openalex.org/W1970504153","https://openalex.org/W1977272108","https://openalex.org/W1989155278","https://openalex.org/W1992989752","https://openalex.org/W2024458092","https://openalex.org/W2040492000","https://openalex.org/W2095614026","https://openalex.org/W2112731915","https://openalex.org/W2124535811","https://openalex.org/W2143935213","https://openalex.org/W2154422144","https://openalex.org/W2162026804","https://openalex.org/W2167667767","https://openalex.org/W2168676389","https://openalex.org/W2896729416","https://openalex.org/W2922291523","https://openalex.org/W2963902795","https://openalex.org/W2982438521","https://openalex.org/W2988016530","https://openalex.org/W2990620332","https://openalex.org/W3006359319","https://openalex.org/W3007263895","https://openalex.org/W3015830560","https://openalex.org/W3023276187","https://openalex.org/W3033934310","https://openalex.org/W3040438640","https://openalex.org/W3092253227","https://openalex.org/W3120682017","https://openalex.org/W3186685722","https://openalex.org/W3205128006","https://openalex.org/W4214571698","https://openalex.org/W4220944423","https://openalex.org/W4246015031","https://openalex.org/W4254460084","https://openalex.org/W4297026089","https://openalex.org/W4307725072","https://openalex.org/W4372228239","https://openalex.org/W4392097111","https://openalex.org/W4401416089","https://openalex.org/W4401423834","https://openalex.org/W4404724409","https://openalex.org/W4405248305"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,28],"article,":[2],"we":[3,68],"propose":[4],"a":[5,134,178],"novel":[6],"method":[7,125,166],"for":[8],"camera":[9,120],"calibration":[10,61,100],"based":[11],"on":[12],"genetic":[13,51],"programming":[14,52],"and":[15,35,77,94,111,142,158,182],"pseudo-linearized":[16],"symbolic":[17,65,150],"regression":[18,57],"\u2014":[19],"an":[20,33,168],"approach":[21],"that":[22,39],"has":[23],"been":[24],"little":[25],"explored":[26],"in":[27,70,74],"context.":[29],"Our":[30],"technique":[31],"evolves":[32],"explicit":[34,116],"analyzable":[36],"mathematical":[37,156],"function":[38],"maps":[40],"3D":[41],"world":[42],"points":[43],"to":[44],"2D":[45],"image":[46],"coordinates.":[47],"The":[48,164],"proposed":[49,165],"multi-gene":[50],"framework":[53],"employs":[54],"pseudo-linearization":[55],"of":[56,91,105,118,147,171],"trees,":[58],"decomposing":[59],"the":[60,115,148],"mapping":[62],"into":[63],"interpretable":[64],"sub-functions,":[66],"which":[67],"analyze":[69],"detail.":[71],"This":[72],"results":[73],"superior":[75],"precision":[76],"model":[78],"transparency\u2014key":[79],"advantages":[80],"over":[81,173,177,184],"black-box":[82],"alternatives":[83],"such":[84],"as":[85],"deep":[86],"learning.The":[87],"evolved":[88,149],"function,":[89],"composed":[90],"polynomial,":[92],"trigonometric,":[93],"transcendental":[95],"expressions,":[96],"enables":[97],"more":[98],"accurate":[99],"than":[101],"classical":[102,162],"methods,":[103],"regardless":[104],"environmental":[106],"conditions":[107],"during":[108],"data":[109],"acquisition,":[110],"does":[112],"not":[113],"require":[114],"specification":[117],"intrinsic":[119],"parameters.":[121],"We":[122],"validate":[123],"our":[124],"through":[126],"extensive":[127],"experiments":[128],"using":[129],"Wilcoxon\u2019s":[130],"statistical":[131],"tests":[132],"with":[133,161],"5%":[135],"significance":[136],"level,":[137],"demonstrating":[138],"its":[139],"practical":[140],"feasibility":[141],"efficiency.":[143],"A":[144],"comprehensive":[145],"analysis":[146],"models":[151],"is":[152],"provided,":[153],"examining":[154],"their":[155],"structure":[157],"comparing":[159],"them":[160],"formulations.":[163],"achieved":[167],"average":[169],"improvement":[170],"29\u00d7":[172],"Zhang\u2019s":[174],"method,":[175,186],"670\u00d7":[176],"Genetic":[179],"Algorithm-based":[180],"approach,":[181],"631\u00d7":[183],"Tsai\u2019s":[185],"while":[187],"maintaining":[188],"robust":[189],"performance":[190],"under":[191],"significant":[192],"Gaussian":[193],"noise.":[194]},"counts_by_year":[],"updated_date":"2025-12-24T23:09:58.560324","created_date":"2025-12-17T00:00:00"}